code: def test_ImageRecordIter_seed_augmentation(cifar10):
      seed_aug = 3
  
      def assert_dataiter_items_equals(dataiter1, dataiter2):
          """
          Asserts that two data iterators have the same numbner of batches,
          that the batches have the same number of items, and that the items
          are the equal.
          """
          for batch1, batch2 in zip_longest(dataiter1, dataiter2):
              # ensure iterators contain the same number of batches
              # zip_longest will return None if on of the iterators have run out of batches
              assert (
                  batch1 and batch2
              ), "The iterators do not contain the same number of batches"
  
              # ensure batches are of same length
              assert len(batch1.data) == len(
                  batch2.data
              ), "The returned batches are not of the same length"
  
              # ensure batch data is the same
              for i in range(0, len(batch1.data)):
                  data1 = batch1.data[i].asnumpy().astype(np.uint8)
                  data2 = batch2.data[i].asnumpy().astype(np.uint8)
                  assert np.array_equal(data1, data2)
  
      def assert_dataiter_items_not_equals(dataiter1, dataiter2):
          """
          Asserts that two data iterators have the same numbner of batches,
          that the batches have the same number of items, and that the items
          are the _not_ equal.
          """
          for batch1, batch2 in zip_longest(dataiter1, dataiter2):
              # ensure iterators are of same length
              # zip_longest will return None if on of the iterators have run out of batches
              assert (
                  batch1 and batch2
              ), "The iterators do not contain the same number of batches"
  
              # ensure batches are of same length
              assert len(batch1.data) == len(
                  batch2.data
              ), "The returned batches are not of the same length"
  
              # ensure batch data is the same
              for i in range(0, len(batch1.data)):
                  data1 = batch1.data[i].asnumpy().astype(np.uint8)
                  data2 = batch2.data[i].asnumpy().astype(np.uint8)
                  if not np.array_equal(data1, data2):
                      return
          assert (
              False
          ), "Expected data iterators to be different, but they are the same"
  
      # check whether to get constant images after fixing seed_aug
      dataiter1 = mx.io.ImageRecordIter(
          path_imgrec=os.path.join(cifar10, "cifar", "train.rec"),
          mean_img=os.path.join(cifar10, "cifar", "cifar10_mean.bin"),
          shuffle=False,
          data_shape=(3, 28, 28),
          batch_size=3,
          rand_crop=True,
          rand_mirror=True,
          max_random_scale=1.3,
          max_random_illumination=3,
          max_rotate_angle=10,
          random_l=50,
          random_s=40,
          random_h=10,
          max_shear_ratio=2,
          seed_aug=seed_aug,
      )
  
      dataiter2 = mx.io.ImageRecordIter(
          path_imgrec=os.path.join(cifar10, "cifar", "train.rec"),
          mean_img=os.path.join(cifar10, "cifar", "cifar10_mean.bin"),
          shuffle=False,
          data_shape=(3, 28, 28),
          batch_size=3,
          rand_crop=True,
          rand_mirror=True,
          max_random_scale=1.3,
          max_random_illumination=3,
          max_rotate_angle=10,
          random_l=50,
          random_s=40,
          random_h=10,
          max_shear_ratio=2,
          seed_aug=seed_aug,
      )
  
      assert_dataiter_items_equals(dataiter1, dataiter2)
  
      # check whether to get different images after change seed_aug
      dataiter1.reset()
      dataiter2 = mx.io.ImageRecordIter(
          path_imgrec=os.path.join(cifar10, "cifar", "train.rec"),
          mean_img=os.path.join(cifar10, "cifar", "cifar10_mean.bin"),
          shuffle=False,
          data_shape=(3, 28, 28),
          batch_size=3,
          rand_crop=True,
          rand_mirror=True,
          max_random_scale=1.3,
          max_random_illumination=3,
          max_rotate_angle=10,
          random_l=50,
          random_s=40,
          random_h=10,
          max_shear_ratio=2,
          seed_aug=seed_aug + 1,
      )
  
      assert_dataiter_items_not_equals(dataiter1, dataiter2)
  
      # check whether seed_aug changes the iterator behavior
      dataiter1 = mx.io.ImageRecordIter(
          path_imgrec=os.path.join(cifar10, "cifar", "train.rec"),
          mean_img=os.path.join(cifar10, "cifar", "cifar10_mean.bin"),
          shuffle=False,
          data_shape=(3, 28, 28),
          batch_size=3,
          seed_aug=seed_aug,
      )
  
      dataiter2 = mx.io.ImageRecordIter(
          path_imgrec=os.path.join(cifar10, "cifar", "train.rec"),
          mean_img=os.path.join(cifar10, "cifar", "cifar10_mean.bin"),
          shuffle=False,
          data_shape=(3, 28, 28),
          batch_size=3,
          seed_aug=seed_aug,
      )
  
      assert_dataiter_items_equals(dataiter1, dataiter2)
  
imports: import mxnet
  import mxnet.ndarray
  from mxnet.test_utils import *
  from mxnet.base import MXNetError
  import numpy
  import os
  import gzip
  import pickle
  import time
  import sys
  from common import assertRaises
  import pytest
  from itertools import zip_longest
label_desc: Use image node_id 17 for calling a global function with example usage: test_ImageRecordIter_seed_augmentation(cifar10) without return types